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Downregulation of the histone methyltransferase SETD2 promotes imatinib resistance in chronic myeloid leukaemia cells.

ABSTRACT: OBJECTIVES:Epigenetic modifiers were important players in the development of haematological malignancies and sensitivity to therapy. Mutations of SET domain-containing 2 (SETD2), a methyltransferase that catalyses the trimethylation of histone 3 on lysine 36 (H3K36me3), were found in various myeloid malignancies. However, the detailed mechanisms through which SETD2 confers chronic myeloid leukaemia progression and resistance to therapy targeting on BCR-ABL remain unclear. MATERIALS AND METHODS:The level of SETD2 in imatinib-sensitive and imatinib-resistant chronic myeloid leukaemia (CML) cells was examined by immunoblotting and quantitative real-time PCR. We analysed CD34+ CD38- leukaemic stem cells by flow cytometry and colony formation assays upon SETD2 knockdown or overexpression. The impact of SETD2 expression alterations or small-molecule inhibitor JIB-04 targeting H3K36me3 loss on imatinib sensitivity was assessed by IC50, cell apoptosis and proliferation assays. Finally, RNA sequencing and ChIP-quantitative PCR were performed to verify putative downstream targets. RESULTS:SETD2 was found to act as a tumour suppressor in CML. The novel oncogenic targets MYCN and ERG were shown to be the direct downstream targets of SETD2, where their overexpression induced by SETD2 knockdown caused imatinib insensitivity and leukaemic stem cell enrichment in CML cell lines. Treatment with JIB-04, an inhibitor that restores H3K36me3 levels through blockade of its demethylation, successfully improved the cell imatinib sensitivity and enhanced the chemotherapeutic effect. CONCLUSIONS:Our study not only emphasizes the regulatory mechanism of SETD2 in CML, but also provides promising therapeutic strategies for overcoming the imatinib resistance in patients with CML.

SUBMITTER: Sheng Y

PROVIDER: S-EPMC6668982 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe Gab2 docking protein acts as an important signal amplifier downstream of various growth factor receptors and Bcr-Abl, the driver of chronic myeloid leukaemia (CML). Despite the success of Bcr-Abl tyrosine kinase inhibitors (TKI) in the therapy of CML, TKI-resistance remains an unsolved problem in the clinic. We have recently shown that Gab2 signalling counteracts the efficacy of four distinct Bcr-Abl inhibitors. In the course of that project, we noticed that two clinically relevant drugs, imatinib and dasatinib, provoke distinct alterations in the electrophoretic mobility of Gab2, its signalling output and protein interactions. As the signalling potential of the docking protein is highly modulated by its phosphorylation status, we set out to obtain more insights into the impact of TKIs on Gab2 phosphorylation.FindingsUsing stable isotope labelling by amino acids in cell culture (SILAC)-based quantitative mass spectrometry (MS), we show now that imatinib and dasatinib provoke distinct effects on the phosphorylation status and interactome of Gab2. This study identifies several new phosphorylation sites on Gab2 and confirms many sites previously known from other experimental systems. At equimolar concentrations, dasatinib is more effective in preventing Gab2 tyrosine and serine/threonine phosphorylation than imatinib. It also affects the phosphorylation status of more residues than imatinib. In addition, we also identify novel components of the Gab2 signalling complex, such as casein kinases, stathmins and PIP1 as well as known interaction partners whose association with Gab2 is disrupted by imatinib and/or dasatinib.ConclusionsBy using MS-based proteomics, we have identified new and confirmed known phosphorylation sites and interaction partners of Gab2, which may play an important role in the regulation of this docking protein. Given the growing importance of Gab2 in several tumour entities we expect that our results will help to understand the complex regulation of Gab2 and how this docking protein can contribute to malignancy.

Project description:BackgroundTreatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction 'rules' for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia.Principle findingsThe common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models.

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Downregulation of the histone methyltransferase SETD2 promotes imatinib resistance in chronic myeloid leukaemia cells.

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